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James J. Collins

Bio: James J. Collins is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Synthetic biology & Population. The author has an hindex of 151, co-authored 669 publications receiving 89476 citations. Previous affiliations of James J. Collins include Baylor College of Medicine & University at Albany, SUNY.


Papers
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Journal ArticleDOI
TL;DR: In a more complex model of cell division, where size-control checkpoints are included, it is shown that the same reversible binding reaction can alter the mean cell mass in a continuously dividing cell.
Abstract: We demonstrate, by using mathematical modeling of cell division cycle (CDC) dynamics, a potential mechanism for precisely controlling the frequency of cell division and regulating the size of a dividing cell. Control of the cell cycle is achieved by artificially expressing a protein that reversibly binds and inactivates any one of the CDC proteins. In the simplest case, such as the checkpoint-free situation encountered in early amphibian embryos, the frequency of CDC oscillations can be increased or decreased by regulating the rate of synthesis, the binding rate, or the equilibrium constant of the binding protein. In a more complex model of cell division, where size-control checkpoints are included, we show that the same reversible binding reaction can alter the mean cell mass in a continuously dividing cell. Because this control scheme is general and requires only the expression of a single protein, it provides a practical means for tuning the characteristics of the cell cycle in vivo.

54 citations

Journal ArticleDOI
TL;DR: RGS2 is identified as a promising target for interfering with neurodegeneration due to LRRK2 mutations in PD patients and found that RGS2 regulates L RRK2 function and neuronal toxicity through its effects on kinase activity and independently of GTPase activity, which reveals a novel mode of action for GAP proteins.
Abstract: Mutations in LRRK2 are one of the primary genetic causes of Parkinson's disease (PD). LRRK2 contains a kinase and a GTPase domain, and familial PD mutations affect both enzymatic activities. However, the signaling mechanisms regulating LRRK2 and the pathogenic effects of familial mutations remain unknown. Identifying the signaling proteins that regulate LRRK2 function and toxicity remains a critical goal for the development of effective therapeutic strategies. In this study, we apply systems biology tools to human PD brain and blood transcriptomes to reverse-engineer a LRRK2-centered gene regulatory network. This network identifies several putative master regulators of LRRK2 function. In particular, the signaling gene RGS2, which encodes for a GTPase-activating protein (GAP), is a key regulatory hub connecting the familial PD-associated genes DJ-1 and PINK1 with LRRK2 in the network. RGS2 expression levels are reduced in the striata of LRRK2 and sporadic PD patients. We identify RGS2 as a novel interacting partner of LRRK2 in vivo. RGS2 regulates both the GTPase and kinase activities of LRRK2. We show in mammalian neurons that RGS2 regulates LRRK2 function in the control of neuronal process length. RGS2 is also protective against neuronal toxicity of the most prevalent mutation in LRRK2, G2019S. We find that RGS2 regulates LRRK2 function and neuronal toxicity through its effects on kinase activity and independently of GTPase activity, which reveals a novel mode of action for GAP proteins. This work identifies RGS2 as a promising target for interfering with neurodegeneration due to LRRK2 mutations in PD patients.

53 citations

Patent
27 Aug 1996
TL;DR: In this article, a signal processor for producing a bias signal and an input device for inputting the bias signal to a sensory cell area associated with the sensory cell whose function is to be enhanced.
Abstract: Method and system for enhancing the function of sensory cells are disclosed The method comprises locating a sensory cell area associated with the sensory cell whose function is to be enhanced and inputting a bias signal to the sensory cell area The apparatus comprises a signal processor for producing a bias signal and an input device for inputting the bias signal to a sensory cell area associated with a sensory cell whose function is to be enhanced Inputting the bias signal to a sensory cell area effectively lowers the threshold of sensory cells with which the sensory cell area is associated

53 citations

Patent
05 Mar 2010
TL;DR: The use of engineered bacteriophages for the reduction of bacterial infections, both in a subject or for bioremediation purposes, in clinical settings and wound healing is discussed in this article.
Abstract: The present invention is generally related to engineered bacteriophages expressing antimicrobial peptides or lytic enymes or fragments thereof for targeting a broad spectum of bacterial hosts, and for the long-term suppression of bacterial phage resistance for reducing bacterial infections. In some embodiments, bacteriophages express antimicrobial peptides or antimicrobial polypeptides (e.g. phage lytic enzymes) which are secreted from the host bacteria, or alternatively released upon lysis of the bacterial host cell. Aspects of the present invention also relate to the use of the engineered bacteriophages for the reduction of bacterial infections, both in a subject or for bioremediation purposes, in clinical settings and wound healing.

52 citations


Cited by
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Journal ArticleDOI
04 Jun 1998-Nature
TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Abstract: Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.

39,297 citations

28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Journal ArticleDOI
TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.
Abstract: The emergence of order in natural systems is a constant source of inspiration for both physical and biological sciences. While the spatial order characterizing for example the crystals has been the basis of many advances in contemporary physics, most complex systems in nature do not offer such high degree of order. Many of these systems form complex networks whose nodes are the elements of the system and edges represent the interactions between them. Traditionally complex networks have been described by the random graph theory founded in 1959 by Paul Erdohs and Alfred Renyi. One of the defining features of random graphs is that they are statistically homogeneous, and their degree distribution (characterizing the spread in the number of edges starting from a node) is a Poisson distribution. In contrast, recent empirical studies, including the work of our group, indicate that the topology of real networks is much richer than that of random graphs. In particular, the degree distribution of real networks is a power-law, indicating a heterogeneous topology in which the majority of the nodes have a small degree, but there is a significant fraction of highly connected nodes that play an important role in the connectivity of the network. The scale-free topology of real networks has very important consequences on their functioning. For example, we have discovered that scale-free networks are extremely resilient to the random disruption of their nodes. On the other hand, the selective removal of the nodes with highest degree induces a rapid breakdown of the network to isolated subparts that cannot communicate with each other. The non-trivial scaling of the degree distribution of real networks is also an indication of their assembly and evolution. Indeed, our modeling studies have shown us that there are general principles governing the evolution of networks. Most networks start from a small seed and grow by the addition of new nodes which attach to the nodes already in the system. This process obeys preferential attachment: the new nodes are more likely to connect to nodes with already high degree. We have proposed a simple model based on these two principles wich was able to reproduce the power-law degree distribution of real networks. Perhaps even more importantly, this model paved the way to a new paradigm of network modeling, trying to capture the evolution of networks, not just their static topology.

18,415 citations

Journal ArticleDOI
TL;DR: In this paper, Imagined communities: Reflections on the origin and spread of nationalism are discussed. And the history of European ideas: Vol. 21, No. 5, pp. 721-722.

13,842 citations

Journal ArticleDOI
Stephen S Lim1, Theo Vos, Abraham D. Flaxman1, Goodarz Danaei2  +207 moreInstitutions (92)
TL;DR: In this paper, the authors estimated deaths and disability-adjusted life years (DALYs; sum of years lived with disability [YLD] and years of life lost [YLL]) attributable to the independent effects of 67 risk factors and clusters of risk factors for 21 regions in 1990 and 2010.

9,324 citations